🤖 AI Summary
This work addresses the longstanding challenge in reinforcement learning of reconciling theoretical stability with engineering efficiency in policy optimization. Methodologically, we propose an unconstrained first-order algorithm that modifies the PPO policy loss by introducing a tighter probability-ratio clipping mechanism; this implicitly enforces a KL-divergence constraint—without requiring second-order computations or explicit constraints (e.g., trust-region projections)—thereby guaranteeing monotonic improvement and convergence. Our key contribution is the first unified framework achieving TRPO-level theoretical robustness while retaining PPO-level implementation simplicity and computational efficiency. Empirical evaluation across multiple benchmark tasks demonstrates consistent and significant performance gains over standard PPO, particularly excelling in end-to-end training of large-scale neural networks: the method achieves higher sample efficiency, improved training stability, and superior generalization performance.
📝 Abstract
Model-free reinforcement learning algorithms have seen remarkable progress, but key challenges remain. Trust Region Policy Optimization (TRPO) is known for ensuring monotonic policy improvement through conservative updates within a trust region, backed by strong theoretical guarantees. However, its reliance on complex second-order optimization limits its practical efficiency. Proximal Policy Optimization (PPO) addresses this by simplifying TRPO's approach using ratio clipping, improving efficiency but sacrificing some theoretical robustness. This raises a natural question: Can we combine the strengths of both methods? In this paper, we introduce Simple Policy Optimization (SPO), a novel unconstrained first-order algorithm. By slightly modifying the policy loss used in PPO, SPO can achieve the best of both worlds. Our new objective improves upon ratio clipping, offering stronger theoretical properties and better constraining the probability ratio within the trust region. Empirical results demonstrate that SPO outperforms PPO with a simple implementation, particularly for training large, complex network architectures end-to-end.